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Applied mathematics, Statistics, Probability, Electrical and electronic engineering, Energy and fuels
Micro-generation technologies such as photovoltaics and micro-wind power are becoming increasing popular among homeowners, mainly a result of policy support mechanisms helping to improve cost competiveness as compared to traditional fossil fuel generation. National government strategies to reduce electricity demand generated from fossil fuels and to meet European Union 20/20 targets is driving this change. However, the real performance of these technologies in a domestic setting is not often known as high time resolution models for domestic electricity load profiles are not readily available. As a result, projections in terms of reducing electricity demand and financial paybacks for these micro-generation technologies are not always realistic. Domestic electricity load profiles are often highly stochastic, influenced by many different independent variables such as environmental, dwelling and occupant characteristics that shape individual customer’s load across a single day. This paper presents a stochastic method for generating electricity load profiles based on the application of a Markov chain process. Electricity consumption was recorded at half hourly intervals over a six month period for five individual Irish dwelling types and used to generate synthetic electricity load profiles. The purpose of this paper is to determine whether Markov chain modelling is an effective way of re-generating electricity load profiles for domestic dwellings and identify shortcomings with this particular technique. The results show that the magnitude component of the load profile can be reproduced effectively whilst the temporal distribution needs to be addressed further.
McLoughlin, F., Duffy, A., Conlon, M.: The Generation of Domestic Electricity Load Profiles through Markov Chain Modelling: 3rd International Scientific Conference on Energy and Climate Change; Conference Proceedings: Athens, Greece. Day 2, pages 18-27. 7-8 October, 2010.